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Multilevel Multiprocess Models for Partnership and Childbearing Event Histories

Multilevel Multiprocess Models for Partnership and Childbearing Event Histories. Fiona Steele, Constantinos Kallis, Harvey Goldstein and Heather Joshi Institute of Education, University of London. Aims of Research. Develop methodology for the analysis of correlated event histories

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Multilevel Multiprocess Models for Partnership and Childbearing Event Histories

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  1. Multilevel Multiprocess Models for Partnership and Childbearing Event Histories Fiona Steele, Constantinos Kallis, Harvey Goldstein and Heather Joshi Institute of Education, University of London

  2. Aims of Research • Develop methodology for the analysis of correlated event histories • Apply in an analysis of the link between partnership stability and childbearing • Examine effect of presence of children on separation and move from cohabitation to marriage, adjusting for correlation between partnership transitions and fertility

  3. Methodology: Overview I • Consider all partnerships for ages 16-42 using multilevel modelling • Estimate simultaneously models for 3 types of partnership transition: • Marriage  Separation • Cohabitation  Separation; Cohabitation  Marriage • Estimate these transitions jointly with model for fertility to allow for potential endogeneity of fertility outcomes

  4. Methodology: Overview II • Use multilevel multistate discrete time event history model (Steele et al. 2004) for partnership transitions • ‘States’ are marriage and cohabitation • Competing risks from cohabiting state • Estimate jointly with model for conceptions within partnerships using simultaneous equation (multiprocess) model (extending Lillard (1993) who considers only marriage) • Multilevel data structure: repeated partnerships and births (level 1) within individuals (level 2) • Estimation using MCMC in MLwiN v.2

  5. Endogeneity of Prior Fertility Outcomes • Interested in effect of presence of children on partnership transitions • But children are prior outcomes of a potentially correlated process (fertility) • There may be factors (some unobserved) which influence decisions about partnership transitions and childbearing • If ignored, effects of interest will be biased

  6. uP (Unobserved) XP(t) (Observed) hP(t): Hazard of partnership transition at time t F(t): Children born beforet hF(t): Hazard of conception at time t XF(t) (Observed) uF (Unobserved) Multiprocess Model of Partnership Transitions and Fertility

  7. Model for Partnership Transitions:Marital Separation

  8. Model for Partnership Transitions:Cohabitation to Separation (r=1) or Marriage (r=2)

  9. Model for Fertility Marriage Cohabitation

  10. Data • 1958 British birth cohort (National Child Development Study) • Partnership (living together for >1 month) and birth histories collected retrospectively at ages 33 and 42. Linked to form history for age 16-42. • Covariates from childhood and adulthood. • Analysis sample: n=5142 women with 1 partner by age 42; n=7032 partnerships and n=9137 “partnership episodes”

  11. Selected Random Effect (Residual) Correlations Across Processes • Separation from marriage and marital conception r = -0.28* (*sig. at 5% level) • Separation from cohabitation and cohabiting conception r = 0.19 • Cohabitation to marriage and cohabiting conception r = 0.59*

  12. Effects of Fertility Variables on Log-odds of Marital Separation

  13. Effects of Fertility Variables on Log-odds of Marrying vs. Staying Cohabiting

  14. Conclusions • Allowing for endogeneity of children conceived within marriage/cohabitation affects substantive conclusions regarding effects of children on move from cohabitation to marriage, but not on partnership dissolution • Multiprocess approach also allows: • Estimation of correlation between unobservables within and across processes • Comparison of effects of covariates within and across processes

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